Package: HCPclust 0.1.2

Menghan Yi

HCPclust: Hierarchical Conformal Prediction for Clustered Data with Missing Responses

Implements hierarchical conformal prediction for clustered data with missing responses. The method uses repeated cluster-level splitting and within-cluster subsampling to accommodate dependence, and inverse-probability weighting to correct distribution shift induced by missingness. Conditional densities are estimated by inverting fitted conditional quantiles (linear quantile regression or quantile regression forests), and p-values are aggregated across resampling and splitting steps using the Cauchy combination test.

Authors:Menghan Yi [aut, cre], Judy Wang [aut]

HCPclust_0.1.2.tar.gz
HCPclust_0.1.2.zip(r-4.7)HCPclust_0.1.2.zip(r-4.6)HCPclust_0.1.2.zip(r-4.5)
HCPclust_0.1.2.tgz(r-4.6-any)HCPclust_0.1.2.tgz(r-4.5-any)
HCPclust_0.1.2.tar.gz(r-4.7-any)HCPclust_0.1.2.tar.gz(r-4.6-any)
HCPclust_0.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
HCPclust/json (API)
NEWS

# Install 'HCPclust' in R:
install.packages('HCPclust', repos = c('https://judywangstat.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/judywangstat/hcp/issues

On CRAN:

Conda:

3.00 score 114 downloads 6 exports 20 dependencies

Last updated from:d14edc3a03. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK120
source / vignettesOK206
linux-release-x86_64OK137
macos-release-arm64OK216
macos-oldrel-arm64OK189
windows-develOK100
windows-releaseOK81
windows-oldrelOK89
wasm-releaseOK114

Exports:fit_cond_density_quantilefit_missingness_propensitygenerate_clustered_marhcp_conformal_regionhcp_predict_targetsplot_hcp_intervals

Dependencies:data.tableDiceKriginggrfjsonlitelatticelmtestMASSMatrixMatrixModelsquantregquantregForestrandomForestRColorBrewerRcppRcppEigensandwichSparseMsurvivalxgboostzoo